CEIG: Spanish Computer Graphics Conference

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Browsing CEIG: Spanish Computer Graphics Conference by Subject "aided design"

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    Cylindrical Transform Slicing of Revolute Parts with Overhangs for Laser Metal Deposition
    (The Eurographics Association, 2022) Montoya-Zapata, Diego; Moreno, Aitor; Ortiz, Igor; Ruiz-Salguero, Oscar; Posada, Jorge; Posada, Jorge; Serrano, Ana
    In the context of Laser Metal Deposition (LMD), temporary support structures are needed to manufacture overhanging features. In order to limit the need for supports, multi-axis machines intervene in the deposition by sequentially repositioning the part. Under multi-axis rotations and translations, slicing and toolpath generation represent significant challenges. Slicing has been partially addressed by authors in multi-axis LMD. However, tool-path generation in multi-axis LMD is rarely touched. One of the reasons is that the required slices for LMD may be strongly non-developable. This fact produces a significant mismatch between the tool-path speeds and other parameters in Parametric space vs. actual Euclidean space. For the particular case of developable slices present in workpieces with cylindrical kernel and overhanging neighborhoods, this manuscript presents a methodology for LMD tool path generation. Our algorithm takes advantage of existing cylindrical iso-radial slicing by generating a path in the (?, z) parameter space and isometrically translating it into the R3 Euclidean space. The presented approach is advantageous because it allows the path-planning of complex structures by using the methods for conventional 2.5-axis AM. Our computer experiments show that the presented approach can be effectively used in manufacturing industrial/mechanical pieces (e.g., spur gears). Future work includes the generation of the machine g-code for actual LMD equipment.
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    Perfect Spatial Hashing for Point-cloud-to-mesh Registration
    (The Eurographics Association, 2019) Mejia-Parra, Daniel; Lalinde-Pulido, Juan; Sánchez, Jairo R.; Ruiz-Salguero, Oscar; Posada, Jorge; Casas, Dan and Jarabo, Adrián
    Point-cloud-to-mesh registration estimates a rigid transformation that minimizes the distance between a point sample of a surface and a reference mesh of such a surface, both lying in different coordinate systems. Point-cloud-to-mesh-registration is an ubiquitous problem in medical imaging, CAD CAM CAE, reverse engineering, virtual reality and many other disciplines. Common registration methods include Iterative Closest Point (ICP), RANdom SAmple Consensus (RANSAC) and Normal Distribution Transform (NDT). These methods require to repeatedly estimate the distance between a point cloud and a mesh, which becomes computationally expensive as the point set sizes increase. To overcome this problem, this article presents the implementation of a Perfect Spatial Hashing for point-cloud-to-mesh registration. The complexity of the registration algorithm using Perfect Spatial Hashing is O(NYxn) (NY : point cloud size, n: number of max. ICP iterations), compared to standard octrees and kd-trees (time complexity O(NY log(NT)xn), NT : reference mesh size). The cost of pre-processing is O(NT +(N3H )2) (N3H : Hash table size). The test results show convergence of the algorithm (error below 7e-05) for massive point clouds / reference meshes (NY = 50k and NT = 28055k, respectively). Future work includes GPU implementation of the algorithm for fast registration of massive point clouds.
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    User-reconfigurable CAD Feature Recognition in 1- and 2-topologies with Reduction of Search Space via Geometry Filters
    (The Eurographics Association, 2019) Corcho, Juan Camilo Pareja; Acosta, Oscar Mauricio Betancur; Ruiz, Oscar E.; Cadavid, Carlos; Casas, Dan and Jarabo, Adrián
    In the context of Computer-Aided Design and Manufacturing, the problem of feature recognition plays a key role in the integration of systems. Until now, compromises have been reached by only using FACE-based geometric information of prismatic CAD models to prune the search domain. This manuscripts presents a feature recognition method which more aggressively prunes the search space with reconfigurable geometric tests. This reconfigurable approach allows to enforce arbitrary confluent tests which are topologic and geometric, with enlarged domain. The test sequence is itself a graph (i.e. not a linear or total-order sequence). Unlike the existing methods which are FACE-based, the present one permits combinations of topologies whose dimensions are 2, 1 or 0. This system has been implemented in an industrial environment. The industrial incarnation allows industry-based customization and is faster when compared to topology-based feature recognition. Future work is required in improving robustness of search conditions and improving the graphic input interface.